The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics

The malignant Glioma immuno-oncology matchmaker: towards data-driven precision medicine using spatially resolved radio-multiomics

Adult and paediatric malignant glioma (GBM and pHGG) remain among the most difficult-to-treat cancers with 5-year survival rates of <5% despite intensive standard-of-care therapy. The differences among patients and the heterogeneous and plastic nature of each individual tumour have resulted in all therapeutic clinical trials failing during the past 20 years. Recently, immunotherapy has been showing great promise, but only in subsets of patients. Identifying those patients cannot be done a priori as biomarkers are still largely missing, nor are we able to follow-up on therapeutic efficacy when patients get treated. The GLIOMATCH project aims at improving the clinical outcome of GBM/pHGG patients by enabling immunology-based patient stratification to empower personalised matching of appropriate immunotherapy, while improving follow-up of clinical responses to existing/novel therapeutics. This will be achieved by integrating spatially resolved, multi-layered tissue maps (using integrated single-cell multiomics), with non-invasive MRI images. This integration will fuel into a novel MRI Radio-multiomics hub, that will be made available to clinical professionals through which they can perform tumour-host based patient stratification and personalised therapy matching while interpreting longitudinal follow-up and treatment efficacy. The proposed data-driven models will be developed by analysing the largest cohort of immuno-oncology (I/O) treated GBM/pHGG patients (n>300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-term surviving GBM patients (n~140), in which various tumour-host niches will be studied in how they respond to I/O perturbations and lead to improved clinical outcome. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental data collection will be used to further refine the machine learning models, while proposing novel treatment options. This action is part of the Cancer Mission cluster of projects on “Understanding (tumour-host interactions)".